import numpy as np
import tensorflow as tf
import cv2


def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')

img_path = 'test_0.png'  #test image
Img = cv2.imread(img_path)
img = cv2.resize(Img,(28,28))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
np_img = img.astype(np.float32)

x_image = tf.reshape(np_img, [-1,28,28,1]) #-1 means arbitrary
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)  #conv1
h_pool1 = max_pool(h_conv1)                               #max_pool1

W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)  #conv2
h_pool2 = max_pool(h_conv2)                               #max_pool2

W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) #fc1

keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)               #dropout

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_predict=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) #fc2 output

sess=tf.InteractiveSession()
saver = tf.train.Saver()
saver.restore(sess, "./model_save.ckpt") #load model file must have ./ with tensorflow1.0
predictions = sess.run(y_predict,feed_dict={keep_prob: 0.5})

predicts=predictions.tolist() #tensorflow output is numpy.ndarray like [[0 0 0 0]]
label=predicts[0]
result=label.index(max(label))
print('result num:')
print(result)
